# Atmos AI > Atmos AI and MCP integrations: connect external AI assistants to Atmos through agent skills, atmos mcp start, multi-CLI MCP export, Atmos Pro MCP, and AWS MCP servers; run AI from Atmos through atmos ai ask/chat/exec, --ai command analysis, API providers, CLI providers, external MCP routing/pass-through, toolchain-aware export, auth-wrapped tools, and MCP+skills pairing - Skill: `cloudposse/atmos-ai` (Agent Skill) - Install (CLI): `npx skillmds@latest add cloudposse/atmos-ai` - Raw SKILL.md: https://api.skillmd.com/api/skills/cloudposse/atmos-ai/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: cloudposse (https://skillmd.com/u/cloudposse) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/cloudposse/atmos-ai --- # Atmos AI and MCP ## Purpose Use this skill when the work is about AI and Atmos together. There are two integration patterns: - **AI uses Atmos**: external AI assistants use Atmos Agent Skills for knowledge and Atmos MCP servers for tools. This includes `atmos mcp start`, `atmos mcp export`, the Atmos MCP server, Atmos Pro MCP, AWS MCP servers, and MCP+skills setup for Claude Code, Codex, Gemini, Cursor, Windsurf, GitHub Copilot, and similar clients. - **Atmos uses AI**: Atmos calls AI providers directly or through local CLI providers. This includes `atmos ai ask`, `atmos ai chat`, `atmos ai exec`, `--ai` command analysis, API providers, CLI providers, external MCP server routing, and CLI provider MCP pass-through. This skill is the coordination layer for AI providers, agent skills, MCP configuration, MCP export, and the "AI inside AI" setup where an external assistant calls Atmos, and Atmos can also call AI. ## Routing | Work | Use | |------|-----| | AI provider setup, `atmos ai`, `--ai`, MCP server/client config, MCP export, agent-skill pairing | Stay in `atmos-ai` | | Project discovery, resolved stacks/components, provenance, query filters, affected analysis | Load `atmos-introspection` | | Terraform plan/apply/deploy/destroy, `--affected`, `--all`, `--query`, CI execution matrices | Load `atmos-terraform` or `atmos-ci` | | Cloud credentials, identities, SSO/OIDC, auth-wrapped MCP servers | Load `atmos-auth` | | Tool binaries for MCP servers, `uvx`/`npx` resolution, Aqua aliases, PATH injection | Load `atmos-toolchain` | ## Atmos Uses AI: Commands and Providers ```bash atmos ai chat atmos ai ask "What stacks do we have?" atmos ai exec "validate stacks" --format json atmos ai sessions list atmos ai skill list # Analyze any Atmos command output with AI atmos terraform plan vpc -s prod --ai atmos terraform plan vpc -s prod --ai --skill atmos-terraform atmos terraform plan vpc -s prod --ai --skill atmos-terraform,atmos-stacks ``` Use API providers for CI/CD and non-interactive automation. Use CLI providers when the user wants to reuse an existing local subscription such as Claude Code, Codex CLI, or Gemini CLI. ```yaml ai: enabled: true default_provider: claude-code providers: claude-code: max_turns: 10 tools: enabled: true ``` ## AI Uses Atmos: MCP and Skills Atmos MCP has two separate capabilities: - **Atmos MCP server**: `atmos mcp start` exposes Atmos AI tools to external clients. - **External MCP connections**: `mcp.servers` lets Atmos or an AI CLI use AWS, cloud, database, or custom MCP servers. The Atmos MCP server is disabled by default. Enabling AI does not enable MCP. ```yaml mcp: enabled: true ai: enabled: true tools: enabled: true ``` Use Atmos Agent Skills with MCP. MCP provides live tools; skills provide Atmos-native knowledge and conventions. | Layer | What it provides | Example | |--------|-------------------------------------------------|--------------------------------------------------| | MCP | **Tools** -- live data and execution capability | "What stacks exist?" -> atmos MCP tool call | | Skills | **Knowledge** -- domain patterns and conventions | "How should I structure cross-stack deps?" -> skill | Without skills, an AI assistant falls back to general training data that may generate invalid YAML, miss features like `!store` / `!terraform.output`, or use wrong CLI flags. With skills, the assistant loads the right Atmos context just before answering. The same prompt -- *"set up cross-stack dependencies with remote state"* -- pulls live data through MCP **and** applies Atmos-native patterns (`!terraform.state`, abstract components, inheritance, [remote-state-bridge](../atmos-migration/references/remote-state-bridge.md)) from the relevant skill. ### Installing Skills: `atmos ai skill` (canonical, cross-client) `atmos ai skill install`/`list`/`update`/`uninstall` is the canonical, cross-client way to manage skills -- it works for every supported client (Claude Code, VS Code/Copilot, Gemini), not just Claude Code. ```bash atmos ai skill list # Browse the bundled catalog + what's installed atmos ai skill install atmos-terraform # Bundled skill, offline, no network/Git needed atmos ai skill install github.com/user/repo # Community skill from GitHub atmos ai skill install # Install every bundled skill at once atmos ai skill update # Refresh installed bundled skills to the latest catalog version atmos ai skill uninstall atmos-terraform ``` Installing a bundled skill copies its content at that point in time -- upgrading the `atmos` binary alone doesn't refresh a skill you already installed. Run `atmos ai skill update` after upgrading Atmos to pick up any bundled skill content that shipped since you installed it. Skills installed from GitHub aren't covered by `update` yet; re-run `install --force` for those. By default the skill is copied into every detected client's project-local skill directory (`.claude/skills/`, `.github/skills/` for VS Code/Copilot, `.gemini/skills/`) with zero extra flags. Use `--client`/`--all-clients` to target specific clients, `--scope user`/`--global` to install into each client's user-level directory instead of the project one, or `--path` to take full manual control of the install location (this skips auto-distribution to clients). See [`atmos ai skill`](https://atmos.tools/cli/commands/ai/skill) for the full flag reference. ### Installing Skills: Claude Code Plugin (Claude Code only) For Claude Code specifically, the skills plugin is a lighter-weight alternative that also wires up marketplace updates: ```bash /plugin marketplace add cloudposse/atmos /plugin install atmos@cloudposse ``` For Codex, Gemini, Cursor, Windsurf, GitHub Copilot, JetBrains Junie, and Amazon Q, see the [AI Agent Skills announcement](https://atmos.tools/changelog/ai-agent-skills) for tool-specific install paths, or use `atmos ai skill install` above, which works for all of them. ## The Three Layers A complete AI assistant setup typically uses three layers of MCP servers, each answering a different question: | Layer | Server(s) | Answers | |------------------|-----------------------------------|-----------------------------------------| | **Defined** | `atmos` (Atmos MCP server) | What's in the stacks, components, repo | | **Deployed** | AWS MCP server suite (awslabs/*) | What's live in the cloud right now | | **Over time** | `atmos-pro` (Atmos Pro MCP) | What changed, when, why, who, drift | Use this framing when helping users decide which servers to enable. Pure stack questions need only the `atmos` server. Live-cloud questions need AWS servers. History/drift/deployment questions need Atmos Pro. ## External MCP Server Configuration Configure servers once in `atmos.yaml`. `atmos mcp export` writes them to per-CLI config files. ```yaml toolchain: aliases: uv: astral-sh/uv # Pin uvx via the Atmos toolchain mcp: enabled: true servers: # Atmos's own MCP server — exposes describe/list/validate as tools atmos: command: atmos args: ["mcp", "start"] description: "Atmos AI tools — stacks, components, validation" # AWS MCP server suite — credentials injected via Atmos Auth aws-docs: command: uvx args: ["awslabs.aws-documentation-mcp-server@latest"] description: "AWS docs (public, no auth)" aws-billing: command: uvx args: ["awslabs.billing-cost-management-mcp-server@latest"] identity: readonly env: { AWS_REGION: us-east-1 } description: "AWS billing summaries" aws-iam: command: uvx args: ["awslabs.iam-mcp-server@latest"] identity: readonly description: "IAM role/policy analysis" ``` The canonical AWS server set (use `identity: readonly` via Atmos Auth for servers that need AWS credentials; `aws-docs` is commonly no-auth): | Server | Purpose | |----------------|----------------------------------------| | aws-docs | Search AWS documentation (no auth) | | aws-knowledge | Managed AWS knowledge base (remote) | | aws-pricing | Real-time pricing and cost analysis | | aws-billing | Billing summaries and payment history | | aws-iam | IAM role/policy analysis | | aws-cloudtrail | Event history and API call auditing | | aws-security | Well-Architected security assessment | | aws-api | Direct AWS CLI (read-only by default) | See `examples/mcp-for-ai-coding-assistants/atmos.yaml` for a working full configuration. ## Atmos Pro MCP Server (HTTP transport) The Atmos Pro MCP server is **HTTP transport** (not stdio), runs at `https://atmos-pro.com/mcp`, and is registered **separately** from `atmos mcp export`. Auth is a one-time browser OAuth (GitHub); short-lived tokens land in the OS keychain. Capabilities: drift detection, deployment history, workflow runs, failed-step logs, audit log, repair recommendations, flapping detection. Register it directly with each AI CLI: ```bash # Claude Code claude mcp add --transport http atmos-pro https://atmos-pro.com/mcp # Gemini CLI gemini mcp add --transport http atmos-pro https://atmos-pro.com/mcp ``` For Codex CLI, append to `~/.codex/config.toml`: ```toml [mcp_servers.atmos-pro] type = "http" url = "https://atmos-pro.com/mcp" ``` ## Auth and Profiles For MCP servers that need cloud credentials, prefer Atmos Auth identities over ambient `AWS_PROFILE` switching. When `identity` is set on an MCP server, Atmos injects isolated credential files and profile env vars into that subprocess. A common pattern is a single `readonly` identity (with `default: true`) used by every AWS-querying MCP server, plus per-domain identities (e.g., `billing-auditor`, `security-audit`) for servers that need different account access: ```yaml auth: providers: sso: kind: aws/iam-identity-center start_url: "https://your-org.awsapps.com/start" region: us-east-1 identities: readonly: kind: aws/permission-set default: true via: { provider: sso } principal: name: ReadOnlyAccess account: { id: "123456789012" } ``` For local use, authenticate once with the relevant identity: ```bash atmos auth login readonly atmos mcp test aws-security ``` For CLI providers, export `ATMOS_PROFILE` when the active profile defines the auth identities or MCP servers: ```bash export ATMOS_PROFILE=managers atmos ai ask "What did we spend on EC2 last month?" ``` Do not add an `atmos auth login` step to non-interactive GitHub OIDC CI unless a specific integration requires it. In CI, set `ATMOS_PROFILE` and let Atmos exchange the OIDC token when the command runs. ## Multi-CLI MCP Export `atmos mcp export` generates MCP client config from `mcp.servers`. The format adapts to the client based on the output path or `--format` flag. | Client | Native config path | Format | Export command | |--------------|-------------------------------------|--------|------------------------------------------------------| | Claude Code | `.mcp.json` (project root) | JSON | `atmos mcp export` | | Gemini CLI | `.gemini/settings.json` | JSON | `atmos mcp export --output .gemini/settings.json` | | Cursor | `.cursor/mcp.json` | JSON | `atmos mcp export --output .cursor/mcp.json` | | Codex CLI | `~/.codex/config.toml` | TOML | `atmos mcp export --output ~/.codex/config.toml` | Claude Code and Gemini share the same JSON schema (`mcpServers` object). Codex uses TOML with `[mcp_servers.]` tables. Exported configs preserve two critical behaviors: - Servers with `identity` are wrapped as `atmos auth exec -i -- ...`. - The exported server `env.PATH` includes the Atmos toolchain PATH so tools like `uvx` and `npx` resolve even when the AI client does not inherit the user's shell environment. Inspect and test exported configurations: ```bash atmos mcp list atmos mcp status atmos mcp tools aws-docs atmos mcp test aws-security atmos mcp export ``` `atmos mcp restart ` validates that the server can stop and start during the command; do not describe it as creating a long-running background service for stdio servers. ## Gemini Trusted Folders Gotcha Gemini's Trusted Folders feature blocks MCP servers in untrusted directories. After `atmos mcp export --output .gemini/settings.json`, the user must trust the folder once via the Gemini UI/settings before the MCP servers will start. Symptom: servers configured correctly but no tools available in Gemini. ## Related Examples - `examples/mcp-for-ai-coding-assistants/` -- canonical full setup: Atmos MCP server + AWS server suite + Atmos Pro, exported to Claude Code / Codex / Gemini, AWS credentials via Atmos Auth. - `examples/mcp/` -- external MCP server config when Atmos itself drives the AI loop (`atmos ai ask`) instead of an external CLI. - `examples/ai-claude-code/` -- use a Claude Pro/Max subscription as Atmos's AI provider (no Anthropic API key). Atmos hosts the conversation; Claude Code provides the model. - `examples/ai/` -- multi-provider Atmos AI setup (Anthropic API, OpenAI API, Ollama). No external CLI; chat with infrastructure from `atmos ai ask`. ## Guardrails - Keep MCP server configuration in `atmos.yaml` so agents, IDEs, and CI share one source of truth. - Pin MCP package versions when repeatability matters; avoid unreviewed `@latest` in production workflows. - Use `atmos toolchain` for binaries that MCP servers need, then rely on export/toolchain PATH injection. - Use `--identity=false`, `off`, `0`, or `no` only when deliberately disabling Atmos Auth for a command. - The exported `.mcp.json` is safe to commit -- it contains no secrets (worst case: IAM role names). Credentials resolve at runtime via `atmos auth exec`. - For `awslabs/*` MCP servers, prefer a single `readonly` identity by default and switch only for servers that genuinely need elevated access. ## Related Reading - [MCP Configuration in Atmos](https://atmos.tools/cli/configuration/mcp) - [Atmos Auth](https://atmos.tools/cli/configuration/auth) - [Atmos Toolchain](https://atmos.tools/cli/configuration/toolchain) - [Atmos MCP Server](https://atmos.tools/ai/mcp-server) - [Atmos Agent Skills](https://atmos.tools/ai/agent-skills) - [Atmos Pro MCP server install](https://atmos-pro.com/mcp/install) - [AWS MCP servers (awslabs/mcp)](https://github.com/awslabs/mcp) - Blog: [Configure MCPs once in Atmos, use it from Claude Code, Codex, and Gemini](https://atmos.tools/blog/mcp-for-ai-coding-assistants)